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We now discuss the macroprogramming formulation in ATaG of temperature gradient monitoring functionality. An ATaG program that modeled this behavior as a neighbor-to-neighborprotocol was discussed as part of the programming idioms in Section 2.5.2. Briefly, each node periodically compares its temperature reading with the reading of its neighboring nodes. If the gradient is above a certain threshold, an alarm notification is sent to a designated root node. There are many ways of expressing this behavior using the ATaG primitives. For instance, a simple centralized version can be defined as shown in Figure 5.1. In this approach, the sampling task on each node produces the temperature reading, and all temperature readings are transmitted to a central supervisor task running on the root node. Centralized solutions are usually undesirable in energy-constrained sensor networks because the cost of transmitting raw data to a central location and performing the processing mr outside the network defeats the purpose of s a t sensor nodes equipped with computational capabilities to perform in-network, on-the-fly processing. An ATaG program that uses hierarchical data collection for gradient monitoring is shown in Figure 5.2. In this approach, the TSampler task is instantiated on each node of the network. The assumption throughout this use case is that all nodes are equipped with both temperature and acoustic sensors, hence the sampler tasks can be instantiated on every single node. The TSampler produces a data item of type Temperature at each invocation. This data item simply encapsulates the temperature reading at that invocation. This data item is also added only to the local data pool as indicated by the output channel

16-4.3

This method was used with String delimiters in 6 to split text into its constituents. The delimiter parameter, however, may also be a regular expression. You can use this to allow for more exibility in the delimiter. In this example, a haiku is split into phrases with slashes. However, you can t be entirely sure if the author will include spaces around the slashes, so you allow for whitespace on either side by splitting on a regular expression, as shown in Example 12-6.

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A fundamental idea in the use of control charts is to collect sample data according to what Shewhart called the rational subgroup concept. Generally, this means that subgroups or samples should be selected so that to the extent possible, the variability of the observations within a subgroup should include all the chance or natural variability and exclude the assignable variability. Then, the control limits will represent bounds for all the chance variability and not the assignable variability. Consequently, assignable causes will tend to generate points that are outside of the control limits, while chance variability will tend to generate points that are within the control limits. When control charts are applied to production processes, the time order of production is a logical basis for rational subgrouping. Even though time order is preserved, it is still possible to form subgroups erroneously. If some of the observations in the subgroup are taken at the end of one 8-hour shift and the remaining observations are taken at the start of the next 8-hour shift,

any differences between shifts might not be detected Time order is frequently a good basis for forming subgroups because it allows us to detect assignable causes that occur over time Two general approaches to constructing rational subgroups are used In the rst approach, each subgroup consists of units that were produced at the same time (or as closely together as possible) This approach is used when the primary purpose of the control chart is to detect process shifts It minimizes variability due to assignable causes within a sample, and it maximizes variability between samples if assignable causes are present It also provides better estimates of the standard deviation of the process in the case of variables control charts This approach to rational subgrouping essentially gives a snapshot of the process at each point in time where a sample is collected.

EXAMPLE 12-6

In the second approach, each sample consists of units of product that are representative of all units that have been produced since the last sample was taken Essentially, each subgroup is a random sample of all process output over the sampling interval This method of rational subgrouping is often used when the control chart is employed to make decisions about the acceptance of all units of product that have been produced since the last sample In fact, if the process shifts to an out-of-control state and then back in control again between samples, it is sometimes argued that the rst method of rational subgrouping de ned above will be ineffective against these types of shifts, and so the second method must be used When the rational subgroup is a random sample of all units produced over the sampling interval, considerable care must be taken in interpreting the control charts.

kernel MultiplyBlendMode <...> { input image4 source1; input image4 source2; output pixel4 result; void evaluatePixel() { result = sample(source1, outCoord()) * sample(source2, outCoord()); } }

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